Experimenting with Causal discovery algorithms (draft)¶
In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from causallearn.search.ConstraintBased.PC import pc
from causallearn.utils.GraphUtils import GraphUtils
import requests
%matplotlib inline
/home/tassilo/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm
In [2]:
!pip install causal-learn
Requirement already satisfied: causal-learn in ./.venv/lib/python3.10/site-packages (0.1.3.3) Requirement already satisfied: numpy in ./.venv/lib/python3.10/site-packages (from causal-learn) (1.23.5) Requirement already satisfied: statsmodels in ./.venv/lib/python3.10/site-packages (from causal-learn) (0.13.5) Requirement already satisfied: pydot in ./.venv/lib/python3.10/site-packages (from causal-learn) (1.4.2) Requirement already satisfied: networkx in ./.venv/lib/python3.10/site-packages (from causal-learn) (2.8.8) Requirement already satisfied: graphviz in ./.venv/lib/python3.10/site-packages (from causal-learn) (0.20.1) Requirement already satisfied: tqdm in ./.venv/lib/python3.10/site-packages (from causal-learn) (4.65.0) Requirement already satisfied: pandas in ./.venv/lib/python3.10/site-packages (from causal-learn) (1.5.3) Requirement already satisfied: scipy in ./.venv/lib/python3.10/site-packages (from causal-learn) (1.10.1) Requirement already satisfied: matplotlib in ./.venv/lib/python3.10/site-packages (from causal-learn) (3.7.1) Requirement already satisfied: scikit-learn in ./.venv/lib/python3.10/site-packages (from causal-learn) (1.1.3) Requirement already satisfied: kiwisolver>=1.0.1 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (1.4.4) Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (23.0) Requirement already satisfied: pillow>=6.2.0 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (9.5.0) Requirement already satisfied: fonttools>=4.22.0 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (4.39.3) Requirement already satisfied: contourpy>=1.0.1 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (1.0.7) Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (2.8.2) Requirement already satisfied: cycler>=0.10 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (0.11.0) Requirement already satisfied: pyparsing>=2.3.1 in ./.venv/lib/python3.10/site-packages (from matplotlib->causal-learn) (3.0.9) Requirement already satisfied: pytz>=2020.1 in ./.venv/lib/python3.10/site-packages (from pandas->causal-learn) (2023.3) Requirement already satisfied: joblib>=1.0.0 in ./.venv/lib/python3.10/site-packages (from scikit-learn->causal-learn) (1.2.0) Requirement already satisfied: threadpoolctl>=2.0.0 in ./.venv/lib/python3.10/site-packages (from scikit-learn->causal-learn) (3.1.0) Requirement already satisfied: patsy>=0.5.2 in ./.venv/lib/python3.10/site-packages (from statsmodels->causal-learn) (0.5.3) Requirement already satisfied: six in ./.venv/lib/python3.10/site-packages (from patsy>=0.5.2->statsmodels->causal-learn) (1.16.0)
In [3]:
import pip
pip.main(['install','causal-learn'])
/home/tassilo/.local/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.
warnings.warn("Setuptools is replacing distutils.")
WARNING: pip is being invoked by an old script wrapper. This will fail in a future version of pip.
Please see https://github.com/pypa/pip/issues/5599 for advice on fixing the underlying issue.
To avoid this problem you can invoke Python with '-m pip' instead of running pip directly.
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: causal-learn in /home/tassilo/.local/lib/python3.10/site-packages (0.1.3.3)
Requirement already satisfied: networkx in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (3.0)
Requirement already satisfied: matplotlib in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (3.5.2)
Requirement already satisfied: scikit-learn in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (1.2.2)
Requirement already satisfied: graphviz in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (0.20.1)
Requirement already satisfied: numpy in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (1.23.1)
Requirement already satisfied: statsmodels in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (0.13.5)
Requirement already satisfied: pandas in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (1.5.0)
Requirement already satisfied: scipy in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (1.10.0)
Requirement already satisfied: pydot in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (1.4.2)
Requirement already satisfied: tqdm in /home/tassilo/.local/lib/python3.10/site-packages (from causal-learn) (4.65.0)
Requirement already satisfied: packaging>=20.0 in /usr/lib/python3/dist-packages (from matplotlib->causal-learn) (21.3)
Requirement already satisfied: pillow>=6.2.0 in /home/tassilo/.local/lib/python3.10/site-packages (from matplotlib->causal-learn) (9.2.0)
Requirement already satisfied: python-dateutil>=2.7 in /home/tassilo/.local/lib/python3.10/site-packages (from matplotlib->causal-learn) (2.8.2)
Requirement already satisfied: fonttools>=4.22.0 in /home/tassilo/.local/lib/python3.10/site-packages (from matplotlib->causal-learn) (4.34.4)
Requirement already satisfied: pyparsing>=2.2.1 in /usr/lib/python3/dist-packages (from matplotlib->causal-learn) (2.4.7)
Requirement already satisfied: cycler>=0.10 in /home/tassilo/.local/lib/python3.10/site-packages (from matplotlib->causal-learn) (0.11.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /home/tassilo/.local/lib/python3.10/site-packages (from matplotlib->causal-learn) (1.4.4)
Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3/dist-packages (from pandas->causal-learn) (2022.1)
Requirement already satisfied: threadpoolctl>=2.0.0 in /home/tassilo/.local/lib/python3.10/site-packages (from scikit-learn->causal-learn) (3.1.0)
Requirement already satisfied: joblib>=1.1.1 in /home/tassilo/.local/lib/python3.10/site-packages (from scikit-learn->causal-learn) (1.2.0)
Requirement already satisfied: patsy>=0.5.2 in /home/tassilo/.local/lib/python3.10/site-packages (from statsmodels->causal-learn) (0.5.3)
Requirement already satisfied: six in /usr/lib/python3/dist-packages (from patsy>=0.5.2->statsmodels->causal-learn) (1.16.0)
[notice] A new release of pip available: 22.3 -> 23.0.1 [notice] To update, run: python3 -m pip install --upgrade pip
Out[3]:
0
In [7]:
def get_dataset(url):
response = requests.get(url)
df = pd.read_csv(url, header=None)
return df
In [8]:
df = pd.read_csv("Absenteeism_at_work.csv", delimiter=";")
In [12]:
def churn_dataset(df, pValue=0.01):
# take in dfset url from uci or similar
# take in url of the dfset?
# remove columns that are non-numeric
# remove all instances that contain nan
# move to numpy
# run pc on churned df
include = ['number','bool']
on_included_cols = list(df.select_dtypes(exclude=include).columns)
df = df.select_dtypes(include= include)
d = df.dropna().to_numpy()
print(type(df))
# run pc algo:
var_names = [x for x in list(df.columns) if not x in non_included_cols]
cg = pc(data,pValue,node_names=var_names)
cg.draw_pydot_graph()
pyd = GraphUtils.to_pydot(cg.G)
pyd.write_png('simple_test.png')
In [13]:
churn_dataset(df)
<class 'pandas.core.frame.DataFrame'>
--------------------------------------------------------------------------- NameError Traceback (most recent call last) Input In [13], in <cell line: 1>() ----> 1 churn_dataset(df) Input In [12], in churn_dataset(df, pValue) 12 print(type(df)) 13 # run pc algo: ---> 14 var_names = [x for x in list(df.columns) if not x in non_included_cols] 15 cg = pc(data,pValue,node_names=var_names) 17 cg.draw_pydot_graph() Input In [12], in <listcomp>(.0) 12 print(type(df)) 13 # run pc algo: ---> 14 var_names = [x for x in list(df.columns) if not x in non_included_cols] 15 cg = pc(data,pValue,node_names=var_names) 17 cg.draw_pydot_graph() NameError: name 'non_included_cols' is not defined
In [73]:
churn_dataset(df,pValue=0.05)
<class 'numpy.ndarray'>
Depth=5, working on node 20: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 21/21 [00:00<00:00, 234.51it/s]
In [ ]:
In [ ]:
In [14]:
amphi = pd.read_csv("amphibiens.csv",delimiter=";")
In [15]:
to_remove = ["ID", "Motorway"]
amp = amphi.drop(to_remove, axis=1).dropna().to_numpy()
In [16]:
amp
Out[16]:
array([[600, 1, 1, ..., 0, 0, 0],
[700, 1, 5, ..., 0, 1, 0],
[200, 1, 5, ..., 0, 1, 0],
...,
[500, 1, 1, ..., 0, 1, 0],
[300, 1, 12, ..., 0, 0, 0],
[300, 1, 12, ..., 0, 0, 0]])
In [17]:
var_names = [x for x in list(amphi.columns) if not x in to_remove]
In [29]:
from causallearn.search.ConstraintBased.PC import pc
from causallearn.search.ConstraintBased.FCI import fci
cg = pc(amp,node_names=var_names)
#cg.draw_pydot_graph()
from causallearn.utils.GraphUtils import GraphUtils
pyd = GraphUtils.to_pydot(cg.G)
pyd.write_png('simple_test.png')
Depth=3, working on node 20: 100%|█████████████| 21/21 [00:00<00:00, 738.06it/s]
In [33]:
var_names
Out[33]:
['SR', 'NR', 'TR', 'VR', 'SUR1', 'SUR2', 'SUR3', 'UR', 'FR', 'OR', 'RR', 'BR', 'MR', 'CR', 'Green frogs', 'Brown frogs', 'Common toad', 'Fire-bellied toad', 'Tree frog', 'Common newt', 'Great crested newt']
In [34]:
from causallearn.search.ConstraintBased.PC import pc
from causallearn.search.ConstraintBased.FCI import fci
G, edges = fci(amp,node_names=var_names)
#.draw_pydot_graph()
from causallearn.utils.GraphUtils import GraphUtils
pyd = GraphUtils.to_pydot(G,edges,labels=var_names)
pyd.write_png('simple_test2.png')
Depth=3, working on node 20: 100%|█████████████| 21/21 [00:00<00:00, 597.71it/s]
X9 --> X8 X19 --> X17 X18 --> X19 X20 --> X18 X19 --> X21 X21 --> X20
In [51]:
ba2.head()
Out[51]:
| university | overall | support 1 | support2 | exams | support3 | students | ma_time | ba_time | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | RWTH Aachen | 1.6 | 0.857143 | 1.9 | 1.8 | 1.8 | 2010 | 54.4 | 78.5 |
| 1 | Uni Bamberg | 1.7 | 0.785714 | 1.9 | 2.0 | 1.8 | 2230 | 61.2 | 90.2 |
| 2 | FU Berlin | 2.3 | 0.785714 | 2.4 | 2.2 | 2.1 | 1530 | 67.5 | 82.0 |
| 3 | HU Berlin | NaN | 0.714286 | 2.2 | 2.1 | 2.3 | 1270 | 46.0 | 66.4 |
| 4 | TU Berlin | NaN | NaN | NaN | NaN | NaN | 520 | 91.3 | 84.8 |
In [68]:
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np
--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Input In [68], in <cell line: 1>() ----> 1 import dowhy 2 from dowhy import CausalModel 3 import pandas as pd ModuleNotFoundError: No module named 'dowhy'
In [ ]:
In [66]:
sns.heatmap(ba2.corr(numeric_only=True),annot=True, cmap='coolwarm')
plt.show()
In [ ]: